image - 如何在 OpenCV Python 中对附近的轮廓进行分组？ - 斑马线检测

Question

我想检测斑马线。我试图使用 contour 找出图像中斑马线的坐标，但它给出了不同白框的输出(斑马线中只有白线)。但是我需要整个斑马线的坐标。

请告诉我轮廓分组的方法或建议我另一种检测斑马线的方法。

Input image

Output image obtained

Expected output

import cv2
import numpy as np
image = cv2.imread('d.jpg',-1)
paper = cv2.resize(image,(500,500))
ret, thresh_gray = cv2.threshold(cv2.cvtColor(paper, cv2.COLOR_BGR2GRAY),
                        200, 255, cv2.THRESH_BINARY)
image, contours, hier = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
for c in contours:
    rect = cv2.minAreaRect(c)
    box = cv2.boxPoints(rect)
    # convert all coordinates floating point values to int
    box = np.int0(box)
    cv2.drawContours(paper, [box], 0, (0, 255, 0),1)
cv2.imshow('paper', paper)
cv2.imwrite('paper.jpg',paper)
cv2.waitKey(0)

Answer 1

您可以 closing用于缩小间隙的形态学操作。

我猫建议以下阶段:

• 在 thresh_gray 中查找轮廓。
• 用非常小的区域(噪声)删除轮廓。
• 删除低纵横比的轮廓(假设斑马线必须又长又窄。
• 使用morphologyEx执行关闭形态学操作 - 关闭合并关闭组件。
• 在删除和关闭后的图像中再次找到轮廓。
  在最后阶段，忽略小轮廓。

这是一个工作代码示例:

import cv2
import numpy as np

image = cv2.imread('d.jpg', -1)
paper = cv2.resize(image, (500,500))
ret, thresh_gray = cv2.threshold(cv2.cvtColor(paper, cv2.COLOR_BGR2GRAY), 200, 255, cv2.THRESH_BINARY)
image, contours, hier = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

# Erase small contours, and contours which small aspect ratio (close to a square)
for c in contours:
    area = cv2.contourArea(c)

    # Fill very small contours with zero (erase small contours).
    if area < 10:
        cv2.fillPoly(thresh_gray, pts=[c], color=0)
        continue

    # https://stackoverflow.com/questions/52247821/find-width-and-height-of-rotatedrect
    rect = cv2.minAreaRect(c)
    (x, y), (w, h), angle = rect
    aspect_ratio = max(w, h) / min(w, h)

    # Assume zebra line must be long and narrow (long part must be at lease 1.5 times the narrow part).
    if (aspect_ratio < 1.5):
        cv2.fillPoly(thresh_gray, pts=[c], color=0)
        continue


# Use "close" morphological operation to close the gaps between contours
# https://stackoverflow.com/questions/18339988/implementing-imcloseim-se-in-opencv
thresh_gray = cv2.morphologyEx(thresh_gray, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (51,51)));

# Find contours in thresh_gray after closing the gaps
image, contours, hier = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

for c in contours:
    area = cv2.contourArea(c)

    # Small contours are ignored.
    if area < 500:
        cv2.fillPoly(thresh_gray, pts=[c], color=0)
        continue

    rect = cv2.minAreaRect(c)
    box = cv2.boxPoints(rect)
    # convert all coordinates floating point values to int
    box = np.int0(box)
    cv2.drawContours(paper, [box], 0, (0, 255, 0),1)

cv2.imshow('paper', paper)
cv2.imwrite('paper.jpg', paper)
cv2.waitKey(0)
cv2.destroyAllWindows()

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thresh_gray 在删除小的和方形的轮廓之前:

thresh_gray 删除小和方形轮廓后:

thresh_gray 关闭操作后:

最终结果:

---

备注:
我对使用形态学操作来缩小差距的好处有些怀疑。
使用基于几何的智能逻辑可能会更好。